dbt | data build tool ) enables data analysts | Business library
kandi X-RAY | dbt Summary
kandi X-RAY | dbt Summary
Analysts using dbt can transform their data by simply writing select statements, while dbt handles turning these statements into tables and views in a data warehouse. These select statements, or "models", form a dbt project. Models frequently build on top of one another – dbt makes it easy to manage relationships between models, and visualize these relationships, as well as assure the quality of your transformations through testing.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of dbt
dbt Key Features
dbt Examples and Code Snippets
Lines of Code : 102License : Non-SPDX (NOASSERTION)
Copy#!/usr/bin/env bash
TUTORIAL_DIR="$(pwd)/tutorial/"
rm -rf $TUTORIAL_DIR/normalization-files
mkdir -p $TUTORIAL_DIR/normalization-files
docker cp airbyte-server:/tmp/workspace/$NORMALIZE_WORKSPACE/normalize/ $TUTORIAL_DIR/normalization-files
NORMA NORMALIZE_WORKSPACE="5/0/"
#!/usr/bin/env bash
docker run --rm -i -v airbyte_workspace:/data -w /data/$NORMALIZE_WORKSPACE/normalize --network host --entrypoint /usr/local/bin/dbt airbyte/normalization debug --profiles-dir=. --project-dir=.
Running #!/usr/bin/env bash
docker run --rm -i -v airbyte_workspace:/data -w /data/$NORMALIZE_WORKSPACE/normalize --network host --entrypoint /usr/local/bin/dbt airbyte/normalization run --profiles-dir=. --project-dir=.
Running with dbt=0.19.1
Found 4 model my_xcom = kwargs['task_instance'].xcom_pull(
task_ids='my_previous_task_name')
-- my_macro.sql
{% macro get_dependencies(model_name) %}
{% set models = graph.nodes.values() %}
{% set model = (models | selectattr('name', 'equalto', model_name) | list).pop() %}
{% do log("sources: ", info=true) %}
{%# schema.yml
models:
- name: iris
meta:
owner: "@matteo"
fal:
scripts:
- "notify.py"
# notify.py
import os
from slack_sdk import WebClient
from slack_sdk.errors import SlackApiError
CHANNEL[tox]
envlist = py36,py37,py38,py39,integration-synapse,flake8
[testenv]
deps = whatever needed for your unit tests
commands = python -m pytest ...unit tests...
[testenv:integration-synapse]
deps =
pytest
pytest-dbt-adapter
commam1 = df['Value'].eq('CLBD').groupby(df['Group']).transform('any')
m2 = ~df['Name'].str.contains('bal')
df_out = df[m1 | m2].copy()
df_out['Group'] = df_out['Group'].mask(df_out['Name'].str.contains('bal'), 0)
df_out
class CDBTools:
details = {
'server' : 'localhost',
'database' : 'MyDB',
'username' : 'me',
'password' : 'myPass'
}
conn = None
def __init__(self, server, database, username, password):
mail.sendmail( ... , content.encode('utf-8'))
Community Discussions
Trending Discussions on dbt
QUESTION
In my dbt model definition, I want to run a config block, but only if the database type is postgresql. Is there a way to do that? Example model below:
...ANSWER
Answered 2022-Apr-15 at 21:53{{ target.type }} will compile to the name of the adapter you're using. See docs
You can wrap your hook in an {% if target.type == 'postgres' %}...{% endif %} block. That will likely get unweildy, so I recommend creating a macro that accepts a relation as an argument:
QUESTION
I am aware that Google Analytics can be linked to Bigquery using BigQuery Linking features in the GA.
But I experienced the drawback that it's scheduled at a random time. So, it's messed up my table with dependencies to these GA data, which I set up at 9 AM using DBT -- so if the GA data is updated above 9 AM, my table won't have today's GA data.
My questions are:
- Is there a way to schedule the updated GA data to have constant time, as the cronjob did?
- Or if there is not any. Is there a way for DBT to run the job after the GA data is updated on bigquery?
ANSWER
Answered 2022-Apr-01 at 07:16Unfortunately Google provide no SLA on the BigQuery export from Google Analytics 3, if you have the option the best solution would be to migrate to Google Analytics 4, which was an almost realtime export to BigQuery and appears to be much more robust. Find out more on the official Google support page.
I currently get around this by using event based triggers that look at the meta data of a table, or check for the existence of a sharded table for yesterday, then proceed down downstream jobs, I'm sure you could achieve something similar with DBT.
Here is some example SQL code which checks for the existence of yesterday's Google Analytics sharded table by returning the maximum timestamp:
QUESTION
I am trying to connect to a Spark cluster on Databricks and I am following this tutorial: https://docs.databricks.com/dev-tools/dbt.html. And I have the dbt-databricks connector installed (https://github.com/databricks/dbt-databricks). However, no matter how I configure it, I keep getting "Database error, failed to connect" when I run dbt test / dbt debug.
This is my profiles.yaml:
ANSWER
Answered 2022-Feb-21 at 13:12I had not specified this in the original question, but I had used conda to set up a virtual environment. Somehow that doesn't work, so I'd recommend following the tutorial to the letter and use pipenv.
QUESTION
If I run this command on my terminal (https://hub.getdbt.com/dbt-labs/codegen/latest/):
...ANSWER
Answered 2022-Mar-22 at 13:17If you're confident that the output is always structured with those exact two timestamps, you can do:
QUESTION
I am working on migrating SQL server store proc and loading the data to Snowflake using DBT. I am having some issues with using insert within while loop. Would really appreciate if anyone has feedbacks on this.
Error: SQL Compilation error: syntax line 7 position 18 unexpected '('. syntax error line 8 at position 12 unexpected 'insert'.
Temp tables are defined already.
...ANSWER
Answered 2022-Mar-18 at 23:24The syntax needed is slightly different, fixed:
QUESTION
I am writting a Dag and I encountered a problem when I try to extract the Xcom value from the task.
I would like to achieve something like this
- Write a function
functhat gets the value of the manually triggered dag_run parameter in{{ dag_run.conf }}
{"dlf_40_start_at":"2022-01-01", "dlf_40_end_at":"2022-01-02"}, pushes the value to Xcom
...ANSWER
Answered 2022-Feb-26 at 22:39XCOMs work automatically with a return value from a task, so instead of using the DomainDbtCloudJobOperator, you can just return your return_value, and it'll be saved as an XCOM with the task name of where you currently are.
Then in your next task, you just need to do something like:
QUESTION
I am new using DBT so maybe you guys can help me create this macro. I have a macro as the one you can see below:
...ANSWER
Answered 2022-Feb-25 at 04:45How I would structure it to solve this quickly…
QUESTION
I am trying to make an sql file in dbt in order to update models with a new column
...ANSWER
Answered 2022-Feb-22 at 22:05In this particular case, you don’t need to use the statement CREATE OR REPLACE TABLE, to create a materialized table. You only need to write the SELECT statement.
There are no create or replace statements written in model statements. This means that dbt does not offer methods for issuing CREATE TABLE statements which can be used for source tables.
You can see this example.
QUESTION
SELECT ID, VOLUME, TYPEOF(VOLUME) FROM DBT.BASE
ANSWER
Answered 2022-Feb-21 at 11:03use this for only want null recode
QUESTION
We have a large schema.yml file in our DBT folders. It is not the cleanest or easiest to find what we need in it. I am curious if anyone knows of a way to split up this file. I am not trying to overcomplicate things and separate the dbt project into multiple or anything like that but rather just work on cleaning up the schema.yml file for readability etc. Thanks!
...ANSWER
Answered 2022-Feb-17 at 14:18You can split this up as much as one model per file, and call the files whatever you want.
The way I usually do it is one file per model and name the file the same as the model.
Just make sure you have
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